• Foreign policy issues, with subcategories such as
internationalism, foreign special relationships, or
military issues.
• Freedom and law issues, with subcategories such
as human rights, democracy, or constitutionalism.
• Government issues, with subcategories such as
centralization, administrative efficiency, or polit-
ical corruption.
• Economic policy issues, with subcategories such
as technology, infrastructure, growth, or eco-
nomic regulation.
• Social policy issues, with subcategories such as
equality, welfare state, or education.
• Cultural policy issues, with subcategories such as
national way of life, traditional morality, or mul-
ticulturalism.
• Target groups issues, with subcategories such as
appeal to labor groups, farmers, middle class, or
minorities.
The categories listed above can be highly fragmented,
but we believe that this level of generality is still suit-
able to analyze the macro-dynamics of political dis-
course.
The annotated data for the UK is available from
1997 to 2015, for the US the annotated data is avail-
able from 2004 to 2012, for Canada from 2011 to
2015. The time span for each country is extremely
small, covers only a limited number of elections, and
does not allow to see the long-term dynamics of polit-
ical debate. The plain-text data from the US goes as
far back as 1960. We are especially interested in the
US, since we hypothesize that the dynamics of a bi-
partisan system are comparatively simple. One needs
to come up with a way to classify the non-annotated
texts. One needs a classifier that associates each sen-
tence in the non-annotated program to a category from
the list above. Convolutional neural networks are a ro-
bust tool for tasks of this sort. Following the approach
proposed by (Kim, 2014) we train seven binary classi-
fiers on the annotated programs from the UK, the US,
and Canada and apply these classifiers to the histor-
ical programs of the Democratic and the Republican
Party of the United States. Details on the obtained
classifiers are given in the Appendix.
Figure 1 shows the dynamics of seven categories
in the political programs of Democrats and Republi-
cans. Each subfigure shows the percentage of text in
the program addressing a specific issue. These esti-
mates are imperfect (the accuracy of each classifier is
around 70% on the test data), but since we apply the
same classifiers to Republican and Democratic pro-
grams and Figure 1 depicts the percentage of sen-
tences out of the total number of classified sentences,
the visualization captures the qualitative dynamics of
the discourse. Indeed Figure 1 provides several inter-
esting insights.
There are patterns which can be attributed to the
actual history of the political process: In the eight-
ies Democrats start to constantly pay more atten-
tion to social issues; In 2000 there is a rapid in-
crease in Republican attention to culture-related is-
sues. Other interesting patterns are surges of attention
to government-related issues in the Republican pro-
grams of 1964 (after the death of Kennedy) and 1976
(after Watergate). It is interesting that the parties stay
relatively close to each other on each issue. This is
surprising, since we are talking about the percentage
of the program devoted to a certain topic. If we look
closely at presidential election results, we see another
interesting pattern. If we give one point to the pres-
idential candidate whose party has devoted a larger
fraction of their program to a given issue, and give
zero points to the candidate whose party devoted a
smaller percentage to the issue, we see that, in the ma-
jority of the cases, the scores would be 4:3 or 3:4. In
most cases the candidate whose score exceeds the op-
ponent’s wins the election. The scores are not always
close to each other: Richard Nixon and Bill Clinton
win their first elections with 5:2 in 1972 and 1992 re-
spectively. Ronald Reagan is reelected in 1984 with
5:2, on the verge of 6:1.
These empirical results suggest the following ex-
planation. As long as political parties compete across
a number of different issues, the amount of resources
that each party allocates to a given issue is propor-
tional to its success among the voters that pay at-
tention to the issue. The success of one party in a
category can be modeled as ’the winner takes it all’:
The party that allocated more resources to the issue
’wins’ it, while the opposing party ’looses’ the issue.
The party that ’gets the most issues’ wins the elec-
tion. Models with this structure are known in political
science and economics as Colonel Blotto games. In
the following sections we propose the extension of a
Blotto game to model the dynamics of political dis-
course, run a simulation of this model, and briefly dis-
cuss its qualitative behavior.
3 THE TOY MODEL
Mathematical social scientists have used a game-
theoretical model, originally conceived by (Borel,
1921) and typically referred to as the Colonel Blotto
game, to model electoral competition. In the origi-
nal setup of the game the military commander Blotto
is tasked to divide his troops among a finite number
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